SLAM with various representations of the world and different sensors has been thoroughly studied in the literature.
Occupancy grid map with LiDAR or laser range finders is among the early successes that dates back to the 1980s [1]}, [2]}, [3]}, [4]}.
In occupancy based approaches, the world is represented by 2D/3D grids composed of free spaces and occupied spaces. New scans from the
LiDAR or laser range finders are compared and matched with previous scans to incrementally build such maps.
This simplified representation of the world facilitates efficient computation, and thus real-time performance can be achieved on relatively large scenes with a single CPU. However,
The successful matching of two scans relies on geometric features such as corners.
In places that lack such features, like long hallways, SLAM using occupancy grid maps tends to fail.
In recent years, SLAM with 3D dense mapping and RGB-D cameras has become more and more popular [5]}, [6]}, [7]}.
This line of work is able to utilize both the geometric information from depth cameras and the color information from RGB cameras to reconstruct environments in dense 3D maps.
Incoming depth and color images are converted into volumes or deformation surfaces [5]}, then matched with previously constructed volumes or surfaces to incrementally build the map.
3D dense maps provide photographic details of the environment with millions of volumes or surfaces.
However, they rely heavily on parallel computation enabled by GPUs, and do not scale very well.
<FIGURE>